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@article{2367201,
author = {Myska, Vojtech and Genzor, Samuel and Mezina, Anzhelika and Burget, Radim and Mizera, Jan and Stybnar, Michal and Kolarik, Martin and Sova, Milan and Dutta, Malay Kishore},
article_location = {Basel},
article_number = {10},
doi = {http://dx.doi.org/10.3390/diagnostics13101755},
keywords = {personalised medication recommendation algorithms; artificial intelligence; post-COVID syndrome; prediction model; respiratory system; corticosteroids; eHealth},
language = {eng},
issn = {2075-4418},
journal = {Diagnostics},
title = {Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19},
url = {https://www.mdpi.com/2075-4418/13/10/1755},
volume = {13},
year = {2023}
}
TY - JOUR
ID - 2367201
AU - Myska, Vojtech - Genzor, Samuel - Mezina, Anzhelika - Burget, Radim - Mizera, Jan - Stybnar, Michal - Kolarik, Martin - Sova, Milan - Dutta, Malay Kishore
PY - 2023
TI - Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19
JF - Diagnostics
VL - 13
IS - 10
SP - 1-17
EP - 1-17
PB - MDPI
SN - 20754418
KW - personalised medication recommendation algorithms
KW - artificial intelligence
KW - post-COVID syndrome
KW - prediction model
KW - respiratory system
KW - corticosteroids
KW - eHealth
UR - https://www.mdpi.com/2075-4418/13/10/1755
N2 - Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.
ER -
MYSKA, Vojtech, Samuel GENZOR, Anzhelika MEZINA, Radim BURGET, Jan MIZERA, Michal STYBNAR, Martin KOLARIK, Milan SOVA and Malay Kishore DUTTA. Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19. \textit{Diagnostics}. Basel: MDPI, 2023, vol.~13, No~10, p.~1-17. ISSN~2075-4418. Available from: https://dx.doi.org/10.3390/diagnostics13101755.
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